Modeling wheat yield gaps in European Russia using the SWAT model SWAT conference July

Transcription

1 Modeling wheat yield gaps in European Russia using the SWAT model SWAT conference July Schierhorn, Florian Faramarzi, Monireh Prishchepov, Alexander V. Koch, Friedrich Müller, Daniel

2 Rising demand for agricultural products Population o.5 billion more by 7 o Growing affluence Diets o Meat o Processed food Bioenergy o Traditional (8%) o Modern (%) guardian.co.uk guardian.co.uk guardian.co.uk

3 Two options to increase agricultural production 1. Expansion of agricultural land o Most fertile land already used o Further expansion associated with high environmental tradeoffs. Intensification on existing agricultural land o More inputs per unit of land o Better technology 3

4 Low yields due to yield gap?

5 Objectives Simulation of post-soviet wheat yields using SWAT [->capturing of inter-anual yield variations] Simulation of wheat yield potentials Sensitivity analysis of growing period (& PHU) and crop parameters on yield

6 Study area 3 E E 5 E E 7 E Vologda Perm Kirov Udmurtia European Russia MariEl Nizhny Chuvash # * Moscow Ryazan Tatarstan Bashkortostan Ulyanovsk Mordovia Samara Tula 5 N Penza Bryansk Oryol Lipetsk Tambov Saratov Kursk # * Belgorod Voronezh 5 N Volgograd Ukraine 5 subbasins -> 3 Kazakhstan selected for individual calibration Rostov Spring wheat Subbasin Krasnodar E Stravropol Provinces (oblast) 5 E E

7 Model setup Calibration: 199- / 3yrs warm-up Validation: / 3yrs warm-up Study area size: million km² square kilometers Hargreaves method for ET-estimation Dominant soil and land use property Winter wheat & spring wheat dominant Assignment of ONE representative subbasin per province (oblast) -> 3 regions to be calibrated indi

8 Data Landuse: Binary cropland map (1 ha) Soil: Harmonized World Soil Database (1 km) Climate: Daily weather data from Schuol and Abbaspour (7) Wheat yield: ROSSTAT 1 (provinces) Management: Growing period USDA N fertilizer ROSSTAT 1

9 Calibration, Validation, and scenarios analysis Wheat yields were calibrated for each province Simulated wheat yields were compared with anual wheat yield data reported at provincial level After satisfactory calibration/validation, two scenarios: A) sufficient N fertilizer, B) sufficient N fertilizer + Water were constructed [to access yield potentials]

10 Initial parameter ranges for calibration Parameter MIN MAX Parameter MIN MAX v ESCO.hru.1 1 v USLE_K(1).sol.5 v EPCO.hru.1 1 v USLE_K().sol.5 v OV_N.hru.1.8 v SOL_EC(1).sol 1 v LAT_TTIME.hru 18 v SOL_EC().sol 1 v LAT_SED.hru 5 v SHALLST.gw 1 v CANMX.hru 1 v DEEPST.gw 19 v SOL_BD(1).sol v GW_DELAY.gw 5 v SOL_BD().sol v ALPHA_BF.gw 1 v SOL_CBN(1).sol 1 3 v GWQMN.gw 5 v SOL_CBN().sol v GW_REVAP.gw.. v SOL_ALB(1).sol.5 v RCHRG_DP.gw 1.1 v SOL_ALB().sol.5 v GWHT.gw 5 v ANION_EXCL.sol..8 v GW_SPYLD.gw. v SOL_K(1).sol v SHALLST_N.gw 1 v SOL_K().sol v GWSOLP.gw 1 v SOL_CRK.sol 1 v HLIFE_NGW.gw 35 v CN.mgt 5 9 r FRT_KG{x,x}.mgt Crop related parameters were excluded for calibration!

11 Parameter FRT_KG{1,3}.mgt SOL_CBN().sol FRT_KG{9,3}.mgt FRT_KG{,3}.mgt FRT_KG{11,3}.mgt FRT_KG{1,3}.mgt FRT_KG{8,3}.mgt ESCO.hru FRT_KG{1,3}.mgt CN.mgt FRT_KG{7,3}.mgt FRT_KG{13,3}.mgt SOL_CBN(1).sol FRT_KG{15,3}.mgt FRT_KG{,3}.mgt ANION_EXCL.sol EPCO.hru FRT_KG{5,3}.mgt CANMX.hru SOL_BD(1).sol SOL_BD().sol SOL_K(1).sol GWSOLP.gw FRT_KG{3,3}.mgt GWQMN.gw SOL_K().sol LAT_SED.hru SHALLST_N.gw FRT_KG{1,3}.mgt SOL_EC(1).sol DEEPST.gw GWHT.gw LAT_TTIME.hru FRT_KG{,3}.mgt SOL_ALB(1).sol SOL_EC().sol GW_REVAP.gw HLIFE_NGW.gw OV_N.hru GW_SPYLD.gw SOL_ALB().sol USLE_K(1).sol RCHRG_DP.gw ALPHA_BF.gw USLE_K().sol SHALLST.gw SOL_CRK.sol GW_DELAY.gw Parameter sensitivity -

12 Calibration and Validation results Calibration Validation

13 kg N No Rainfed irrigation kg N No Rainfed irrigation PHU: 9 YLD =.7 t/ha PHU: 9 YLD = 1. t/ha kg N Irrigation kg N Irrigation PHU: 9 YLD =.7 t/ha PHU: 19 YLD = 3. t/ha

14 Yield gap for sufficient N & Water supply Bashkortostan Belgorod Bryansk Chuvash Kirov Krasnodar Kursk Lipetsk MariEl Mordovia Moscow Nizhny Oryol Penza Perm Rostov Ryazan Samara Saratov Stravropol Tambov Tatarstan Tula Udmurtia Ulyanovsk Volgograd Vologda Voronezh Wheat yield (t/ha) Yield potential for sufficient N & WATER supply Yield potential for sufficient N supply YG N&Water YG N Reported

15 Average wheat yields, 1-3 E E 5 E E 7 E Vologda Kirov Perm Spring wheat European Russia Udmurtia MariEl Nizhny #* Moscow Chuvash Tatarstan Bashkortostan Bryansk Ryazan Tula Oryol Lipetsk Tambov Mordovia Penza Ulyanovsk Samara 5 N 5 N Kursk Belgorod Voronezh Saratov Volgograd Average wheat yields reported 1- t/ha Ukraine Rostov Krasnodar Stravropol E 5 E E

16 Yield gap for sufficent N supply 3 E E 5 E E 7 E Vologda Kirov Perm Spring wheat European Russia Udmurtia MariEl #* Moscow Nizhny Chuvash Tatarstan Bashkortostan Bryansk Ryazan Tula Oryol Lipetsk Tambov Mordovia Penza Ulyanovsk Samara 5 N 5 N Kursk Belgorod Voronezh Saratov Volgograd Average yield gap for sufficient N supply, 1- t/ha Ukraine Rostov Krasnodar Stravropol E 5 E E

17 Yield gap for sufficent N supply & water supply 3 E E 5 E E 7 E Vologda Kirov Perm Spring wheat European Russia Udmurtia MariEl Nizhny #* Moscow Chuvash Tatarstan Bashkortostan Tula Ryazan Mordovia Penza Ulyanovsk Samara 5 N Bryansk Oryol Lipetsk Tambov 5 N Kursk Belgorod Voronezh Volgograd Saratov Average yield gap for sufficient N & Water supply, 1- t/ha Ukraine Rostov Krasnodar Stravropol E Yield gap for entire European Russia:.1 t/ha -> under sufficient N supply 3. t/ha -> under sufficient N & Water supply 5 E E

18 Yield sensitivity to growing period Bashkortostan Chuvash Kirov MariEl Mordovia Nizhny Penza Perm Samara Saratov Tatarstan Udmurtia Ulyanovsk Volgograd Vologda PPU of simulated yield, average growing period 95PPU of simulated yield, short growing period 95PPU of simulated yield, long growing period

19 Yield sensitivity to crop parameter Mordovia Nizhny Penza Samara Saratov Tatarstan Ulyanovsk Volgograd PPU of simulated yield for sufficient N & Water supply, Variation of crop parameters 95PPU of simulated yield for sufficient N & Water supply, SWAT default crop parameters Reported

20 Conclusion Russia has large potential to increase crop yields SWAT crop growth module reproduces accurate yields despite the large scale & data scarcity Yield gaps are heterogeneously distributed Information on crop characteristics (PHU in particular) are essential for accurate yield gap estimations

21 Thank you very much. Contact: Florian Schierhorn Foto: J. Stahl

22 Calibration and Validation results Bashkortostan Chuvash Kirov MariEl Calibration Validation Mordovia Nizhny Penza Perm Calibration Validation Samara Saratov Tatarstan Udmurtia Calibration Validation Volgograd Vologda P factor Calibration Validation R factor R Nash Sutclif